Wind power prediction using a three stage genetic ensemble and auxiliary predictor

This paper presents a novel method for accurate wind power prediction by applying computational intelligence approaches while exploiting Auxiliary Predictor (AxP) and Genetic Programming (GP) based ensemble of Neural Networks (AxP-GPNN). The inherent fluctuations in the power generated by wind mills may affect their optimal integration in the electric grid and therefore, accurate prediction is highly desired. To cater these fluctuations and highly nonlinear mapping, we present an ensemble approach, where the auxiliary predictor is constructed with Radial Basis Function (RBF) network and Relevance Vector Machine (RVM) and various neural networks are then employed as base regressors. Use of RVM is based on its established advantages for robust prediction on unseen data to address the overfitting issue in training phase. AxP is used for suitable weight initialization to base predictors and provides initial decision space to base learners. Further, an ensemble of neural networks based on GP is developed which utilizes the base predictions of neural networks as well as the auxiliary information generated by AxP. The GP ensemble based forecasting engine is thus robust to minor variations in the data as compared to the individual base regressors. We also employ information-theoretic feature selection on physical measurements of the wind mills. Results have been extracted in the form of statistical performance indices including mean absolute error, standard deviation error and mean square error. These error measures are compared with the other existing wind power prediction techniques. These results present better wind power estimates and reduced prediction error. Paired t-test for the proposed model with other machine learning based models is carried out for further evaluation. Overall, these comparisons validate the importance of auxiliary predictor in ensemble model of GP and ANNs. •A three stage prediction mechanism is proposed to reduce variations in the predicted wind power.•Exploration of GP to utilize prediction spaces of 1st and 2nd stages (auxiliary & base learners).•RVM overcomes the over fitting issues in training and improves the generalization on test data.•AxP provides initial decision space to base learners and GP.•Reduced prediction variation bottom-up in the multistage and ensemble learning mechanism.